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BIOSENSICS LLC

Address

57 CHAPEL ST STE 200
NEWTON, MA, 02458-1080
USA

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UEI: C9MRZK4B7K39

Number of Employees: 23

HUBZone Owned: No

Woman Owned: No

Socially and Economically Disadvantaged: No

SBIR/STTR Involvement

Year of first award: 2009

Phase I Awards

Phase II Awards

75.86%

Conversion Rate

$8,876,788

Phase I Dollars

$45,317,388

Phase II Dollars

$54,194,176

Total Awarded

Awards

Up to 10 of the most recent awards are being displayed. To view all of this company's awards, visit the Award Data search page.

A Multi-Modal Remote Measurement Platform for Decentralized Clinical Trials

Amount: $2,000,000 Topic: 22

Conducting clinical trials for both rare and common diseases involve many challenges, some of the most frequent of which include the identification, enrollment, and retention of study participants. Virtual, decentralized, remote or site agnostic trials may be used to include individuals in studies that previously would have been excluded. Data that is collected will also need to be seamlessly captured and integrated from multiple sources and be of sufficient quality to meet regulatory requirements. Mobile technology offers the opportunity for remote participation and monitoring of study subjects, as well as remote and reliable data capture. Development and use of these technologies have the potential to allow for broader and more diverse study populations for participation in clinical trials for rare diseases while simultaneously improving the quality of data capture and efficiency of the trials. BioSensics will develop a robust and integrated multi-modal platform for data capture that can be used to assess individuals at their home and in remote settings for use in clinical trials. Specific Aims The objective of this project is to develop a robust and integrated multi-modal platform for data capture that can be used to assess individuals at their home and in remote settings for use in clinical trials. The technology will be reliable, secure, and easy to use to monitor study participants remotely, either in the home or while participating in activities of daily living. The proposed platform will allow measurements of motor, speech and cognitive functions using sensors and digitized clinical tests, as well as electronic.

SBIR

Phase II

2024

HHS

NIH

ODAlert: Wearable Device for Automatic Detection of Opioid-Induced Respiratory Arrest

Amount: $400,000 Topic: NIDA

In response to the escalating opioid crisis in the United States, there is an urgent need for effective intervention strategies. Opioid overdoses can induce dangerous respiratory depression, leading to critical hypoxia and potential mortality. Current harm reduction measures include the provision of naloxone to drug users, their supporters, and first responders to swiftly counteract opioid effects. However, these interventions may be insufficient given that more than half of opioid-induced fatalities occur at home, often unwitnessed. To address this urgent public health issue, BioSensics and Baylor College of Medicine (BCM) present ODAlert—an innovative wearable device designed to detect and alert users and first responders of potential opioid overdoses. Affixed to the user's abdomen via a biocompatible patch, ODAlert continually monitors respiratory patterns, issuing an auditory alarm and contacting first responders upon detection of respiratory cessation. As initial steps for developing ODAlert, we performed stakeholder interviews with 10 adults with opioid use disorder (OUD) and 10 clinical experts who treat OUD to guide the design of ODAlert such as sensor placement, form factor, and functionality. Based on these surveys, an optimal sensor was identified; namely, a wearable device affixed to the user's abdomen via a biocompatible patch. We developed algorithms for detection of respiratory cessation using a reference wearable device on the abdomen with over 92% sensitivity and specificity in a 10-adult pilot study. Moreover, we submitted a Pre-submission request to the FDA (Q222234) and received feedback about the clinical validation study design to support a De Novo application for ODAlert. Encouraged by these initial results, we are now applying for Blueprint funding to facilitate development, clinical validation, market readiness, FDA approval and commercialization of ODAlert. In Phase I, we will complete the development of ODAlert sensor, optimize battery, deploy real-time algorithms, and secure IDE and IRB approval. In Phase II, we will complete the development of ODAlert and its mobile app, conduct a clinical validation study with opioid- experienced adults (n=30) via a cross-over randomized study, and also evaluate ODAlert's performance in a 6- week study on adults (n=60) with OUD for submission as a part of the De Novo application to the FDA. ODAlert can have significant multiple-level impact: 1) Public health: It introduces a warning system for opioid overdoses, aiming to reduce mortality rates; 2) Technological innovation: ODAlert represents a leap in wearable healthcare tech, inspiring potential life-saving devices; 3) Policy and regulatory: Securing FDA approval for ODAlert can inform policy around wearable-based health devices and set a precedent; 4) Clinical practice: If widely accepted, ODAlert will transform care standards for overdose-prone patients; 5) Patient self-management: ODAlert can empower individuals with OUD by providing a tool to manage their condition and heighten overdose awareness, potentially enhancing health outcomes; and 6) Future Research: The study could foster new research directions in wearable health technology, like an automatic naloxone injection system.

SBIR

Phase I

2024

HHS

NIH

FY24 SBIR PHASE I TOPIC NO. 459 PROJECT TITLE: AN AUTOMATED SCREENING PLATFORM FOR EARLY IDENTIFICATION OF MALNUTRITION IN CANCER PATIENTS

Amount: $400,000 Topic: 459

This project aims to develop an automated screening platform for early identification of malnutrition in cancer patients by creating a machine-learning-based model that combines CT imaging input and questionnaire-based tools. The proposed solution will incorporate novel biomedical image segmentation tools based on artificial intelligence to assess body composition from CT scans. Accurate skeletal muscle and adipose tissue segmentation will be used with current questionnaire-based parameters such as BMI changes and food aversion to develop thresholds for detecting malnutrition.

SBIR

Phase I

2024

HHS

NIH

A Multi-Modal Wearable Sensor for Early Detection of Cognitive Decline and Remote Monitoring of Cognitive-Motor Decline Over Time

Amount: $500,011 Topic: NIA

ABSTRACT Recent demographic changes have led to the emergence of the so-called “dementia epidemic”. Dementia causes great stress to medical, social, and informal care and is currently affecting approximately 55 million persons worldwide. This number is projected to increase to 75.6 million by 2030 and 135.5 million by 2050. Alzheimer’s disease (AD) is a degenerative brain disease and the most common form of dementia. During the last 15 years, more than 400 different AD clinical trials have been conducted for managing AD symptoms, with a combined failure rate of 99.6%. A lack of robust tools for measurement and monitoring of cognitive ability and function is considered a key contributor to the very high failure rate. Additionally, early diagnosis of cognitive decline and measuring its subtle progression over time enables early intervention to prevent further cognitive and functional decline. Finally, access to conventional cognitive screening tools is often limited for those with high socioeconomic deprivation and individuals living in remote areas. Thus, there is an urgent need for a robust solution to identify individuals in the earliest stages of cognitive decline and measure digital biomarkers associated with subtle changes in cognitive-motor performance over time. In this Fast-Track SBIR study, we propose to develop PAMSys+, a multi-modal wearable sensor for remote monitoring of cognitive-motor decline based on measuring dual-task (DT) physical activities (e.g., walking while talking) and digital speech biomarkers during activities of daily living. PAMSys+ includes 1) bi-directional microphones (located inwards to and outward from the chest) with synchronized audio signal data collection, and 2) an accelerometer to collect chest motion data. It combines the capabilities of audio and motion monitoring to create a first-in-kind wearable sensor for remote monitoring of DT physical activities using a patented method developed by our team (U.S. Patent App. No. 17/807,104). PAMSys+ also analyzes audio signals to measure digital speech biomarkers that are shown to be correlated with cognitive function (e.g., speech duration and rate). In Phase I, we will develop a prototype of PAMSys+ and demonstrate its proof of concept in 50 older adults (age 60+) including individuals with and without cognitive impairments (25 per group). Additionally, we will assess the acceptability of PAMSys+ by surveying the participants and a focused expert panel (n=10) using the technology acceptance model (TAM). In Phase II, we will complete the development of PAMSys+ and will study its validity for early detection of AD and also remote monitoring of cognitive-motor decline over time by recruiting 100 adults (age 60+) including 50 individuals with amnestic mild cognitive impairment (aMCI) or mild to moderate AD and 50 age-matched cognitive intact individuals. BioSensics is a 2020 Tibbetts Award winner with a proven track record of rapidly transitioning applied governmental research funding into real commercial results. The proposed project is well aligned with the long-term objectives of BioSensics and is a critical component of our broader plans for development of novel technologies to improve the lives of older adults.

SBIR

Phase I

2023

HHS

NIH

Software Platform for Automatic, Opportunistic Screening of Vertebral Compression Fractures

Amount: $1,972,000 Topic: NIA

PROJECT SUMMARY / ABSTRACT Vertebral compression fracture (VCF) is the most common type of osteoporotic fracture. VCF burdens include, but are not limited to pain, functional impairment, increased risk of future fractures, medical costs and mortality. Only 1/3 of osteoporotic VCFs are symptomatic, and the remaining cases are found incidentally, thus, the burden of VCF disease is significantly underestimated. Despite this, around 700,000 osteoporotic VCFs are diagnosed annually in the US alone, resulting in an estimated annual economic burden of $13.8B. With an aging population, the rate of VCFs and its associated burdens are expected to rise. Therefore, it is of utmost importance to develop screening tools for VCF assessment and identifying individuals at risk of VCF. In this Direct Phase II SBIR project, BioSensics, in collaboration with Beth Israel Deaconess Medical Center will develop a cloud-based platform for automatic, opportunistic analysis of CT images that include spine. The solution will stand by in the central imaging data server of a hospital, investigate each non-investigated spine CT study, and flag the studies of patients that are detected to have osteoporosis or vertebra(e) at risk for fracture. The clinician providing care for the patient will then be prompted to consider ordering a screening for vertebral body compression fractures, bone mineral study or both, given the red flag from the analysis. Upon placing an order, a full report will be presented to the physician. This process is reimbursable under two Common Procedural Technology (CPT) codes that are relevant to the use of the proposed solution (CPT Code 77078 for bone mineral study using computed tomography, and Code 0X36T for reporting an automated analysis of an existing computed tomography study for vertebral fractures). The existing reimbursement CPT code and the significant added value of the proposed solution for hospitals and clinical institutions - in terms of generating additional direct revenue from using the solution as well as providing better care to patients at risk – will facilitate commercialization of the solution. In the longer term, the proposed imaging analysis technology can be used for automatic analysis of thousands of medical images that are taken every day in hospitals and clinics. This will enable detection of diseases and conditions at early stages (e.g., bone metastasis and different tumors), thus facilitating preventive measures and better care for those individuals at risk.

SBIR

Phase II

2023

HHS

NIH

Tele-FootX: Virtually Supervised Tele-Exercise Platform for Accelerating Plantar Wound Healing

Amount: $275,000 Topic: NIA

Abstract Foot ulceration is the most common and costly late complication of diabetes, with morbidity and mortality being worse than many cancers. It is estimated that up to one-third of people with diabetes will develop a diabetic foot ulcer (DFU) in their lifetime. Non-healing DFUs are a leading cause of hospitalization, amputation, disability, and death among people with diabetes. In the United States, one-third of all diabetes-related costs are spent on diabetic foot care, with two-thirds of the costs incurred in inpatient settings, constituting a substantial economic burden to society. Therefore, every means possible should be used to try to heal DFU and prevent amputation. In this regard, there is a significant body of evidence related to the clinical benefits of exercise for people with DFU, including improving blood flow and oxygen supply, muscle loss prevention, and joint mobility. Despite this evidence, exercise is not part of the standard care for wound healing, mainly as there is no solution for promoting and managing home-based exercise programs for people with DFU. In this Phase I SBIR project, we will design an interactive foot and ankle exercise program for people with DFU using wearable sensors called Tele-FootX™. Tele-FootX will enable both remotely and virtually supervised evidence-based foot and ankle exercises and allow clinicians to educate, monitor, and coach patients. The gaming features of the platform will promote adherence to the prescribed exercise programs, and, subsequently, wound healing. This approach is supported by our proof-of-concept study, where we demonstrated the benefit of interactive foot and ankle exercises to improve lower extremity perfusion and increase activity in the calf muscle. To achieve this goal, in Aim 1, we will design an interactive game-based exercise platform using foot- mounted sensors and make it suitable for people with DFU by including game-based exercises inspired by the Buerger-Allen exercise program. In Aim 2, we will demonstrate the acceptability, feasibility, safety, and proof of concept effectiveness of the Tele-FootX in improving lower extremity perfusion by recruiting 15 participants with DFU. We will examine the perceived benefit, ease of use, technology acceptance, usability, and technology anxiety from the point of view of the 15 participants with DFU recruited in Aim 2, as well as 10 health care professionals with expertise in DFU management. In Phase II, we will introduce new gaming features to improve engagement, as well as features for clinicians to educate, manage and personalize the home-based exercise program for each patient. We will also integrate the proposed solution into existing hospital records and remote assessments (e.g., EPIC). We will validate the final product in a 12-week randomized control trial. There are existing CPT codes that cover tele-exercise programs. The existence of reimbursement codes (CPT codes 98975, 98977, 98980, and 98981), the large size of the market (26.1 million worldwide develop DUF annually), and the significant clinical benefits of the proposed solution will facilitate commercialization of Tele-FootX.

SBIR

Phase I

2023

HHS

NIH

Automatic, Opportunistic Surveillance of Hip Bone Fragility in X-ray Images

Amount: $1,884,699 Topic: NIA

PROJECT SUMMARY / ABSTRACT Approximately 1 in 3 women and 1 in 5 men over the age of 50 will suffer from a fragility fracture in their remaining lifetime. Fragility hip fracture is one of the most serious and debilitating outcomes of osteoporosis with a 20–40% mortality rate during the first year after the fracture. Hip fracture incidence rates are known to increase exponentially with age in both women and men; and with the rising life expectancy throughout the globe, the number of men and women who will be above the threshold of fragility fracture is expected to almost double, with a prediction of 319 million cases by 2040. Thus, the number of fractures is predicted to double as well. In this Direct Phase II SBIR, BioSensics, in collaboration with orthopaedic, radiology, endocrinology and biomechanics experts at Harvard Medical School, proposes to develop a cloud-based software solution for automatic, opportunistic screening for hip fracture risk using plain X-ray images, called XFx. X-ray studies are ubiquitous in all corners of the world, are inexpensive and provide high resolution studies that offer insight into bone geometry, microstructure and density, at a low ionizing radiation dose. The proposed software solution will include 1) a desktop application for uploading X-ray images and displaying and visualizing XFx results, and 2) a secure cloud-based backend for receiving the uploaded X-ray images and performing the analysis. The software architecture will support on-premise integration with a hospital cloud services (e.g., PACS systems) to enable automatic, opportunistic screening for hip fracture risk using plain X-ray images. The solution will stand by in the central imaging data server of hospitals or clinics, investigate each non-investigated X-ray image, and if recognized to include a proximal femur, automatically execute the AI/ML-based classification scheme to identify patients with osteoporosis or at high risk of hip fracture. If a patient is identified to have osteoporosis or a high risk of fracture, the software will flag the patient. The clinician providing care for the patient will then be prompted to consider ordering an evaluation of fragility fracture risk and receive a full report. This process is reimbursable under the Current Procedural Terminology (CPT) code 76499 “Unlisted Diagnostic Radiographic Procedure.” This code is used when no other specific procedure code exists. The existence of this CPT will support the initial marketing of the proposed solution. In Phase III, we will prepare an application for a new Category III CPT code and submit if for consideration by the American Medical Association (AMA) CPT Editorial Panel. Given the clinical need of the proposed solution, and recent approval of a CPT code for radiology artificial intelligence (code 0691T) for automated analysis of existing imaging studies for vertebral fracture and bone density assessment, our application for a new CPT code should not face any difficulties. In the longer term, the proposed imaging analysis technology can be used for automatic analysis of thousands of medical images that are taken every day in hospitals and clinics. This will enable detection of diseases and conditions at early stages (e.g., bone metastasis and different tumors), thus facilitating preventive measures and better care for those individuals at risk.

SBIR

Phase II

2023

HHS

NIH

A Multi-Modal Remote Monitoring Platform for Frontotemporal Lobar Degeneration Syndromes

Amount: $2,546,364 Topic: NIA

Project Summary/Abstract Frontotemporal lobar degeneration (FTLD) syndromes span the spectrum of neurodegenerative disorders affecting movement and cognitive function and are pathologically related to Alzheimer's disease. In this project, we will develop a robust multi-modal platform for remote monitoring of motor symptoms, speech and cognitive function in FTLD syndromes using wearable sensors and digitized tests. We will validate this solution in Progressive Supranuclear Palsy (PSP) by collecting longitudinal data from 60 PSP patients over a period of 12 months. PSP is a severe and rapidly progressive FTLD syndrome that lacks effective treatment and leads to rapid onset of dementia, disability and death. The rapid progression of PSP will allow us to validate the solution within the timeline of this Direct Phase II SBIR project and facilitate future studies in FTLD syndromes. In this Direct Phase II project, we will recruit 60 participants with PSP from 2 leading CurePSP Centers of Care. These participants will be monitored for 12 months. The sensor data will be collected using PAMSys, a wearable sensor developed by the support from a NIH STTR grant award. PAMSys is patented (U.S. Patents # 8,206,325, 9,005,141, and 9,901,209), validated and commercialized by BioSensics. The digitized tests will be collected using BioDigit Home, a unified solution for the collection of digital biomarkers that is currently being used in several NIH-funded studies and pharma-sponsored clinical trials in neurological disorders. BioSensics has already customized its BioDigit Home solution to monitor motor, speech and cognitive functions in PSP, and has carried out a pilot study with 7 PSP patients, who were monitored for up to 3 months using the developed system. We will collect data from 60 PSP patients in this aim to develop a set of algorithms for monitoring motor, speech and cognitive function that will enable objective assessment of PSP disease severity and progression, thereby creating a multi-modal remote monitoring solution for PSP. These tools will accelerate clinical trials that are focused on the development of novel therapeutics for tauopathies (i.e., FTD, PSP CBS) and can be readily adapted to many neurodegenerative diseases, including Parkinson's disease, multiple system atrophy, amyotrophic lateral sclerosis, myotonic dystrophy and stroke. BioSensics is a 2020 Tibbetts Award winner with a proven track record of rapidly transitioning applied governmental research funding into real commercial results. The initial market for our technology is pharmaceutical clinical trials, where BioSensics is a vendor of record of medical-grade wearable sensors and digital clinical trial technologies for multiple pharmaceutical companies. The current project will significantly broaden BioSensics' offerings to pharmaceutical companies, specifically by providing a solution for digital assessments of speech, motor and cognition function, which are relevant to many diseases. This commercial potential is fully aligned with the latest recommendations from the FDA, NSF and NIH to digitize clinical trials.

SBIR

Phase II

2022

HHS

NIH

TOPIC 022 - TECHNOLOGICAL DEVELOPMENT AND VALIDATION OF REMOTE MEASURES FORUSE IN CLINICAL TRIALS IN INDIVIDUALS WITH RARE DISEASES

Amount: $325,000 Topic: 22

SBIR

Phase I

2022

HHS

NIH

TeleExergame: An Interactive Tele-Rehabilitation Platform for Improving Motor Function in Older Adults with Cognitive Deficit

Amount: $2,646,167 Topic: NIA

ABSTRACT Many individuals with mild cognitive impairment (MCI) and/or mild dementia experience both cognitive deficits and decline in motor function and postural balance. This results in an increased risk of falls. Conventional balance programs are not tailored for patients with cognitive impairment. Motor-cognitive exercises that are developed for these patients can be effective, but these rehabilitation programs are underutilized. Many patients who were referred exercise programs never attend; even fewer complete prescribed programs. For patients with cognitive impairment, transportation and scheduling are some of the key challenges that limit their adherence and commitment. However, unsupervised in-home exercise programs are not adequate for these individuals because of poor adherence, apathy, and inability to follow exercise instructions. Therefore, there is a need for a remotely- supervised in-home exercise program for older adults with MCI and/or mild dementia. To address this need, we propose to develop a tele-exercise system that allows a qualified therapist to remotely supervise and interact with the patient during goal-oriented game-like and low risk exercise tasks (TeleExergame) that have been designed to improve balance and cognition. The exercises are interactive balance tasks with explicit goal-oriented augmented visual feedback (i.e., the patient’s movement controls a virtual object on the screen, thus providing real-time visual and audio feedback that is critical for engagement and motor learning). This is achieved by a wearable sensor worn on a body segment of interest (e.g., shin) that measures body joint kinematics (e.g., position of foot/ankle during a virtual obstacle crossing task). For this Fast-Track submission, we conducted a preliminary study in a cohort of 22 patients with amnestic MCI, which demonstrated the effectiveness of the proposed Exergame training paradigm in the target population. In Phase I, we will create the first prototype of an easy-to-use Exergame solution for in-home use that includes a telemedicine interface to assist the patient or his/her caregiver with sensor positioning, running the program, remote supervision, and monitoring of the patients during exercise. We will evaluate the feasibility, perception of benefit, and ease of use of the proposed system in the target population and focus group of therapists. In Phase II, we will complete the development of the TeleExergame system to enable HIPAA compliant remote supervision by a qualified therapist and provide quantitative metrics for remote monitoring and personalization of the exercise program by the therapist. We will then conduct a clinical study to evaluate the efficacy of the solution. The proposed interactive tele-rehabilitation system would have an immediate and appreciable impact on patient care by providing an interactive exercise program for individuals with MCI and/or mild dementia. However, MCI and dementia represent only a fraction of the potential market for the proposed technology, which could also be used in other patient populations that exhibit poor balance, such as Parkinson’s disease, diabetics, stroke survivors with lower-extremity paresis, and older adults who cannot perform conventional exercises.

SBIR

Phase II

2022

HHS

NIH